Load
Use baseline level from DBP profiling
Prepare sample background annotations
Number of samples
## [1] 73
Associations with P-value < 0.05
## # A tibble: 4 × 3
## # Groups: PC, feature [4]
## PC feature p.value
## <chr> <chr> <dbl>
## 1 PC2 NOTCH1 0.00129
## 2 PC2 IGHV.status 0.00286
## 3 PC1 trisomy12 0.0102
## 4 PC2 Methylation_Cluster 0.0356
Plot associations
Plot feature loadings on the first three PCs
If multiple concentrations are identified as significant, only show
the most significant concentration.
Prepare patient genomic background
## [1] "IGHV.status" "del11q" "del13q" "del17p" "trisomy12" "NOTCH1" "SF3B1" "TP53"
Test for Genomics
Methylation cluster
If multiple concentrations are identified as significant, only show
the most significant concentration.
Test for Genomics
## # A tibble: 7 × 4
## feature p.value estimate p.adj
## <chr> <dbl> <dbl> <dbl>
## 1 ABT199 0.0174 11.7 0.122
## 2 BAD 0.0460 9.51 0.161
## 3 BIM 0.414 2.99 0.580
## 4 FS1 0.190 4.23 0.333
## 5 HRKy 0.946 -0.0745 0.946
## 6 MS1 0.787 1.49 0.918
## 7 PUMA 0.116 6.85 0.270
##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: patAnno$IGHV.status and patAnno$pretreat
## X-squared = 4.9003, df = 1, p-value = 0.02685
How many treated and untreated samples?
##
## no yes
## 55 18
Test for Genomics
Methylation cluster
Combine
Test for Genomics
Methylation cluster
Table for comparing results
RNAseq
BH3 profiling
## # A tibble: 7 × 2
## feature n
## <chr> <int>
## 1 ABT199 110
## 2 BAD 64
## 3 BIM 0
## 4 FS1 0
## 5 HRKy 0
## 6 MS1 0
## 7 PUMA 0
Combine plot for ABT199 and BAD
Record siginificant RNAs for later feature selection
Proteomics
## [1] 3314 30
BH3 profiling
None passed 10% FDR
BH3 profiling
RNAseq
For genomic data
For demographic and clinical data
Function to Generate the explanatory dataset for each BH3 profile
Clean and integrate multi-omics data
Function for multi-variate regression
Perform lasso regression
Function for the heatmap plot
Plot all heatmaps